Abstract A032: Probabilistic statistical-based model for determining individual cancer risk for making personal recommendations to prevent cancer
Authors: Dmirtii K. Chebanov, Vsevolod A. Misyurin
Journal: Cancer Research (2022)

Abstract About 42% of cancer cases in the US are linked to modifiable risk factors, and thus could be preventable, says a study from American Cancer Society [1]. This proportion can be represented as the percentage by which an individual's possibility of developing cancer can be reduced. Having data on the prevalence of various types of cancer, it is possible to rank risk factors, and give an individual a personalized list of recommendations sorted by importance in terms of preventing cancer. For most commonly cancers, a set of modifiable risk factors is known: for example, for colon cancer these are the following: excess body weight, physical inactivity, smoking, high consumption of red or processed meat, alcohol intake, low intake of calcium, fruits and vegetables and whole-grain fiber, and the proportion of cases arising from modifiable factors is 54.6% [1]. Since we consider this number to be the percentage by which the risk of disease in an individual can be reduced, in the absence of modifiable factors, the likelihood of this cancer in a person will decrease by 54.6%, respectively. The contribution of each of the factors separately is also known: so, for colon cancer, physical inactivity causes 16.3% cases, alcohol intake – 12.8%, etc. Let’s reduce risks in proportion to each other so that their sum is equal to 54.6%. The total degree of fulfillment of risk factors will be the mathematical expectation of all individual risks, that is the sum of the products of the impact of each risk by its degree of presence. To determine the absolute risk and rank the risks in order of priority of occurrence for person, we take statistics on the probability of developing cancer [2]. So, for the colon cancer the probability of disease for men in the interval birth-49yrs is 0.4%. According to our assumption, this value can be reduced by a maximum of 54.6% provided that all risk factors are excluded. Let’s take a situation when a person has 50% modifiable risk factors. Then the absolute (posterior) risk of disease occurrence, provided that risk factors are reduced, will be calculated with Bayes' theorem, where the numerator will be the product of the a priori disease probability (0.4%) by the disease probability provided that all risk factors are excluded (100%-54.6 %=45.4%), and the denominator will be the total probability of the disease: 100% minus the product of risk reduction value (54.6%) multiplied by the degree of its presence (for us 50%). It will be equal to 0.25%. It's easy to check the correctness of the formula by substituting boundary values: for example, for the degree of presence of risks equal to 100%, the calculated absolute risk will not change and will be 0.4%, whereas in the absence of all risk factors the absolute risk will decrease by 54.6% and will be 0.18%. References: 1. Proportion and Number of Cancer Cases and Deaths Attributable to Potentially Modifiable Risk Factors in the United States; Farhad Islami et al. CA Can J Clin 2. Siegel, RL, Miller, KD, Fuchs, H, Jemal, A. Cancer Statistics, 2021. CA Cancer J Clin. 2021: 71: 7- 33 Citation Format: Dmirtii K. Chebanov, Vsevolod A. Misyurin. Probabilistic statistical-based model for determining individual cancer risk for making personal recommendations to prevent cancer [abstract]. In: Proceedings of the AACR Special Conference on Colorectal Cancer; 2022 Oct 1-4; Portland, OR. Philadelphia (PA): AACR; Cancer Res 2022;82(23 Suppl_1):Abstract nr A032.